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CIG
2005
IEEE
16 years 26 days ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes
MIR
2005
ACM
129views Multimedia» more  MIR 2005»
16 years 24 days ago
Tracking concept drifting with an online-optimized incremental learning framework
Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series...
Jun Wu, Dayong Ding, Xian-Sheng Hua, Bo Zhang
ICML
2004
IEEE
16 years 20 days ago
Active learning using pre-clustering
The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary,...
Hieu Tat Nguyen, Arnold W. M. Smeulders
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 12 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
EDUTAINMENT
2008
Springer
15 years 9 months ago
Teaching Machine Learning to Design Students
Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge t...
Bram van der Vlist, Rick van de Westelaken, Christ...